Parameters concurrent learning and reactionless control in post-capture of unknown targets by space manipulators
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Abstract
This paper studies parameters identification and minimizing base disturbances problems after the space manipulator capturing an unknown target. A concurrent learning algorithm that concurrently uses past motion data points and instantaneous motion data of the system is proposed for the parameters identification. Given a condition for selecting the used past data points as well as a scaling technique to make the parameters have the same magnitude, the concurrent learning algorithm guarantees that parameters identification errors can globally converge to zero at an exponential rate and without the need for satisfying the persistent excitation (PE) condition. An adaptive reactionless control method is proposed based on the passivity theorem and Task-priority method, which ensures that the base attitude is stationary and joint motions satisfy the limits during the system generating excitation motions for the parameters identification. Simulation results verify the effectiveness of the proposed method.
Keywords
Space manipulators Parameters identification Adaptive control Concurrent learning Reactionless controlNotes
Acknowledgements
This work was supported by the Major Program of National Natural Science Foundation of China under Grant Nos. 61690210 and 61690211, the National Natural Science Foundation of China under Grant No. 61603304, and the Fundamental Research Funds for the Central Universities.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
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